Methylation of DNA is widespread and plays a critical role in the regulation of gene expression in development, differentiation and diseases such as multiple sclerosis, diabetes, schizophrenia, aging, and cancers. Methylation in particular gene regions, for example in their promoters, can inhibit the expression of these genes. Recent work has shown that the gene silencing effect of methylated regions is accomplished through the interaction of methylcytosine binding proteins with other structural components of chromatin. which, in turn, makes the DNA inaccessible to transcription factors through histone deacetylation and chromatin structure changes. Differentially methylated CpG islands have long been thought to function as genomic imprinting control regions (ICRs).
Deregulation of imprinting has been implicated in several developmental disorders. Identification of the ICRs in a large number of human genes and their regulation patterns during development can shed light on genomic imprinting as well as other fundamental epigenetic control mechanisms. Moreover, rapid advances in genomics, both in terms of technology, for example, high-throughput low-cost capillary sequencers and microarray technologies, as well as in terms of availability of information, for example, information gained by virtue of whole genome sequencing, bioinformatics tools and databases, have paved the way for new opportunities in epigenetic studies. For example, it is known that random autosomal inactivation is one of the mechanisms that mammals use to achieve gene dosage control, in addition to random X-chromosome inactivation in females and genomic imprinting. However, genes belonging to this category are just now emerging, with only a few identified so far. Technologies are needed that can provide a systematic survey for identification of genes regulated by this kind of random monoallelic expression control, and determination of when and how such genes are regulated through a wide screening of samples from different tissues or different disease stages.
Changes in DNA methylation have been recognized as one of the most common molecular alternations in human neoplasia. Hypermethylation of CpG islands located in the promoter regions of tumor suppressor genes are now firmly established as the most frequent mechanisms for gene inactivation in cancers. In contrast, a global hypomethylation of genomic DNA and loss of IGF imprinting are observed in tumor cells; and a correlation between hypomethylation and increased gene expression has been reported for many oncogenes. In addition, monitoring global changes in methylation pattern has been applied to molecular classification of cancers. Most recently, gene hypermethylation has been associated with clinical risk groups in neuroblastoma and hormone receptor status and response to tamoxifen in breast cancer.
Lung cancer is the second most common cancer among both men and women and is the leading cause of cancer death in both sexes. There is no established early detection test for the disease, and only 15% of lung cancer cases are diagnosed when the disease is localized. The ability to accurately detect malignant cells in a wide range of clinical specimens including sputum, blood, or tissue would provide significant implications for screening high-risk individuals for this cancer.
A Human Epigenome Consortium was formed in 1999 with a mission to systematically map and catalogue the genomic positions of distinct methylation variants. It is likely that large-scale discovery of methylation patterns through de novo DNA sequencing of bisulfite-treated DNA is be carried out in the near future. This would provide a resource for methylation studies analogous to SNP databases for genetic studies, and would be expected to greatly increase the demand for high-throughput, cost-effective methods of carrying out site-specific methylation assays. A publicly accessible database, which carries information about methylation patterns in various biologically significant samples would be the first outcome of these efforts. There is a need for methods for analysis of large sample sets useful for discovering such associations.
Presently, the analysis of DNA methylation patterns in genomic DNA has been significantly hampered by the fact that methylation information is not retained during standard DNA amplification steps such as PCR or biological amplification by cloning in bacteria. Therefore, DNA methylation analysis methods generally rely on a methylation-dependent modification of the original genomic DNA before any amplification step. A battery of DNA methylation detection methods has been developed, including methylation-specific enzyme digestion (Singer-Sam, et al., Nucleic Acids Res. 18(3): 687 (1990), Taylor, et al., Leukemia 15(4): 583-9 (2001)), bisulfite DNA sequencing (Frommer, et al., Proc Natl Acad Sci USA. 89(5): 1827-31 (1992), Feil, et al., Nucleic Acids Res. 22(4): 695-6 (1994)), methylation-specific PCR (MSP) (Herman, et al., Proc Natl Acad Sci USA. 93(18): 9821-6 (1996)), methylation-sensitive single nucleotide primer extension (MS-SnuPE) (Gonzalgo, et al., Nucleic Acids Res. 25(12): 2529-31 (1997)), restriction landmark genomic scanning (RLGS) (Kawai, Mol Cell Biol. 14(11): 7421-7 (1994), Akama, et al., Cancer Res. 57(15): 3294-9 (1997)), and differential methylation hybridization (DMH) (Huang, et al., Hum Mol Genet. 8(3): 459-70 (1999)). However, a need exists to use the methylation pattern to classify and predict different types and stages of cancer, cancer therapeutic outcomes and patient survival through analysis of large sample sets required to discover such associations. This invention satisfies this need and provides related advantages as well.